Learning Edits. Prior work has studied learning edits in both
natural language and programming language. We followed the approach
of explicitly representing edits as sequences with edit actions. Our
edit representation is inspired by
[1]}, [2]}, who
studied learning comment edits based on code edits.
[3]}, [4]}, [5]}, [6]} represented code as ASTs (abstract syntax trees) and the code edits as edit actions over the ASTs rather than tokens. We do not focus on editing
structured data (AST) as it can not be generalized to natural
language, and it can not be easily combined with large pretrained models which are primarily based on sequence of tokens.
